Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Biosignal-Based Computing by AHL Induced Synthetic Gene Regulatory Networks From an in vivo Flip-Flop Implementation to Programmable Computing Agents Thomas Hinze 1 Sikander Hayat 2 Thorsten Lenser 1 Naoki Matsumaru 1 Peter Dittrich 1 {hinze,thlenser,naoki,dittrich}@cs.uni-jena.de, s.hayat@bioinformatik.uni-saarland.de 1 Bio Systems Analysis Group Friedrich Schiller University Jena www.minet.uni-jena.de/csb 2 Computational Biology Group Saarland University www.zbi-saar.de BIOSIGNALS 2008 Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Outline Biosignal-Based Computing by AHL Induced Synthetic GRNs 1. Introduction 2. Gene Regulatory Networks (GRNs) 3. Hill kinetics output regulatory circuit Ptrc gfp cl857 lac I PL* PL* pCIRb pTSM b2 AHL signal 4. Case study: computational units pAHLb Plux Lux I lux I lux R lac I PL* sensor normalised output concentration h 1 0.9 5. RS flip-flop wetlab h + m = 2 0.8 Θ = 5 0.7 0.6 0.5 x*x/(x*x+25) 50% 1-x*x/(x*x+25) implementation in Vibrio fischeri 0.4 0.3 0.2 h −− 0.1 Θ 0 0 5 10 15 20 input concentration x 6. Synthetic GRN for x a b z x y z complex formation x & 0 0 1 z knapsack problem solution RegGeneX RegGeneY EffGene y 0 1 1 1 0 1 y NAND gate 1 1 0 1 Normalised concentration 7. Conclusions, further work 0.8 0.6 Input1: 1 Input1: 1 Input1: 0 Input1: 0 Input2: 0 Input2: 1 Input2: 1 Input2: 0 0.4 0.2 Output 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Time scale Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study computational properties of CSNs/GRNs • Develop new ways to model and predict real CSNs/GRNs • Gain new theoretical perspectives on real CSNs/GRNs Collaboration partner for in vivo studies • BIOTEC at Dresden University of Technology Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study computational properties of CSNs/GRNs • Develop new ways to model and predict real CSNs/GRNs • Gain new theoretical perspectives on real CSNs/GRNs Collaboration partner for in vivo studies • BIOTEC at Dresden University of Technology Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study computational properties of CSNs/GRNs • Develop new ways to model and predict real CSNs/GRNs • Gain new theoretical perspectives on real CSNs/GRNs Collaboration partner for in vivo studies • BIOTEC at Dresden University of Technology Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Motivation and Intention • Computing in vivo • Synthetic/evolutionary predefined computational units • Implementation in micro-organisms • Vision: potentially miniaturised, robust, reliable, energy-efficient and bio-compatible hardware = ⇒ Construction, programming, applicability? www.wikipedia.org Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Biological Principles of Gene Regulation Intercellular Information Processing of Spatial Globality within Organisms gene product Activation (positive gene regulation) signalling substances activation pathway can amplify activation genomic DNA regulator gene effector gene gene expression transcription factor enables gene expression no/few gene product Inhibition (negative gene regulation) signalling substances (inducers) repression pathway can weak repression genomic DNA regulator gene effector gene gene expression transcription factor inhibits gene expression Feedback loops: gene products can act as transcription factors and signalling substances forming gene regulatory networks Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Hill Kinetics – Sigmoid-Shaped Threshold Functions • Model cooperative and competitive aspects of interacting gene regulatory units dynamically and quantitatively • Homogeneous and analytic • Formulate relative intensity of normalised output concentration h 1 0.9 gene regulations by sigmoid- h + m = 2 0.8 Θ = 5 0.7 shaped threshold functions 0.6 h + , h − : R × R × N → R 0.5 x*x/(x*x+25) 50% 1-x*x/(x*x+25) 0.4 • x ≥ 0: input concentration of 0.3 h −− 0.2 transcription factor activating/ 0.1 Θ 0 inhibiting gene expression 0 5 10 15 20 input concentration x • Θ > 0: threshold (50 % level) • m ∈ N + : degree of regulation h + ( x , Θ , m ) x m activation (upregulation) = x m +Θ m h − ( x , Θ , m ) 1 − h − ( x , Θ , m ) inhibition (downregulation) = Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Hill Kinetics – Sigmoid-Shaped Threshold Functions • Model cooperative and competitive aspects of interacting gene regulatory units dynamically and quantitatively • Homogeneous and analytic • Formulate relative intensity of normalised output concentration h 1 0.9 gene regulations by sigmoid- h + m = 2 0.8 Θ = 5 0.7 shaped threshold functions 0.6 h + , h − : R × R × N → R 0.5 x*x/(x*x+25) 50% 1-x*x/(x*x+25) 0.4 • x ≥ 0: input concentration of 0.3 h −− 0.2 transcription factor activating/ 0.1 Θ 0 inhibiting gene expression 0 5 10 15 20 input concentration x • Θ > 0: threshold (50 % level) • m ∈ N + : degree of regulation h + ( x , Θ , m ) x m activation (upregulation) = x m +Θ m h − ( x , Θ , m ) 1 − h − ( x , Θ , m ) inhibition (downregulation) = Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Hill Kinetics – Modelling Dynamical Network Behaviour • Several interacting (competing) transcription factors influence gene expression • Activators A i , inhibitors I j and proportional factor c 1 > 0: determine production rate of a gene product • Additional assumption of I 1 linear spontaneous decay rate I p A 1 c 2 · [ GeneProduct ] with c 2 > 0 A n GeneProduct • Differential equation for corresponding gene product: Gene d [ GeneProduct ] = ProductionRate − c 2 [ GeneProduct ] d t c 1 · h + ( A 1 , Θ A 1 , m ) · . . . · h + ( A n , Θ A n , m ) · = ( 1 − h + ( I 1 , Θ I 1 , m ) · . . . · h + ( I p , Θ I p , m )) − c 2 · [ GeneProduct ] Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
Introduction Hill Kinetics Case Study: Computational Units Implementation in V. fischeri Solving Knapsack Problem Hill Kinetics – Modelling Dynamical Network Behaviour • Several interacting (competing) transcription factors influence gene expression • Activators A i , inhibitors I j and proportional factor c 1 > 0: determine production rate of a gene product • Additional assumption of I 1 linear spontaneous decay rate I p A 1 c 2 · [ GeneProduct ] with c 2 > 0 A n GeneProduct • Differential equation for corresponding gene product: Gene d [ GeneProduct ] = ProductionRate − c 2 [ GeneProduct ] d t c 1 · h + ( A 1 , Θ A 1 , m ) · . . . · h + ( A n , Θ A n , m ) · = ( 1 − h + ( I 1 , Θ I 1 , m ) · . . . · h + ( I p , Θ I p , m )) − c 2 · [ GeneProduct ] Computing by AHL Induced GRNs Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich
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